When should I use k-fold cross-validation?
K-fold cross-validation is useful when you want to assess the performance of a model more reliably. It helps in reducing overfitting.
Can you explain how k-fold cross-validation works?
Sure! In k-fold cross-validation, the dataset is divided into k subsets. The model is trained on k-1 subsets and tested on the remaining one. This process is repeated k times.
What are the advantages of using k-fold cross-validation?
The main advantages include better model evaluation, reduced variance in performance estimates, and more efficient use of data.
Are there any situations where I shouldn't use k-fold cross-validation?
Yes, if your dataset is very small, k-fold might not be ideal as it can lead to high variance in the results. In such cases, simpler methods like train-test split might be better.
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